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Looks at the relevance to economic systems of force field equilibrium metaphors. Points out that the force field view of physical reality developed in nineteenth century physics does not necessarily involve homeostasis and reversible time paths. Illustrates the properties of force fields with hysteresis, providing a counter example. Discusses precedents for viewing economic systems as having equilibria haunted by hysteresis, and summarizes and illustrates the implications of applying such an analytical framework to economic systems.

One of the most vibrant subfields of American intellectual history over the last fifteen years has been the history of the social sciences during the late twentieth century, a period when the size and quality of American social-scientific output grew explosively. Given that the major historiographic push to historicize this period of social science began in the 1990s, in the wake of the collapse of the Soviet Union and the declaration by some Americans of Cold War victory, it was perhaps inevitable that the geopolitics of the Cold War emerged as a major tool for accounting for what was distinct about the social science and broader culture of the postwar period. After all, wasn't it obvious that what made the 1990s different from the decades that came before it was the fact that the Cold War was over? And wasn't it further obvious that the bipolar geopolitics and nuclear night terrors of the Cold War had deformed everything they touched, not least the work of American social scientists? One marker of this obviousness was the transformation of the term "Cold War" from a noun describing (perhaps already too vaguely) a particular sort of geopolitical struggle into an adjective that could explain all sorts of extra-geopolitical activity. By the turn of the century this adjectivalization of the Cold War had become something of a historiographic cliché, a blunt (if not lazy) way to historicize our immediate forebears. When John Lewis Gaddis chose to title his "rethink" of Cold War historyNow We Know, he didn't even need to addBetter.

The acculturation process involved when one organization is acquired by another, and the two organizational cultures merge, has not been adequately conceptualized in the strategic management literature. It is argued here that the acculturation process can be more fully understood by utilizing Lewin's (1951) force-field approach. In addition, major forces of cultural differentiation and organizational integration are identified. It is also argued that the dynamic acculturative change process will both influence and be influenced by postacquisition organizational performance. Predictions as to how post-acquisition performance influences subsequent acculturation modes are offered.

The publication describes a simplified model of the process of fractionation of binary biological mixtures in a closed reference system. The description of the model uses a balance of masses and forces forcing microparticle movement in a base fluid matrix. The movement, consistent with the direction of the excitation forces, was limited to independent migration channels with diameters comparable to the diameters of the migrating microparticles. In this model, the parameters of the densification process of the separated fraction in a single migration channel were applied to the entire volume of the fractioned mixture. The developed model applies to theoretical bases of the erythrocyte sedimentation rate (ESR).
Keywords: biological mixture fractionation, blood component sedimentation, mixture fractionation process model, peripheral blood ESR testing, fractionation in gravity field, fractionation in a centrifugal-force field

Atomistic or ab initio molecular dynamics simulations are widely used to predict thermodynamics and kinetics and relate them to molecular structure. A common approach to go beyond the time- and length-scales accessible with such computationally expensive simulations is the definition of coarse-grained molecular models. Existing coarse-graining approaches define an effective interaction potential to match defined properties of high-resolution models or experimental data. In this paper, we reformulate coarse-graining as a supervised machine learning problem. We use statistical learning theory to decompose the coarse-graining error and cross-validation to select and compare the performance of different models. We introduce CGnets, a deep learning approach, that learns coarse-grained free energy functions and can be trained by a force-matching scheme. CGnets maintain all physically relevant invariances and allow one to incorporate prior physics knowledge to avoid sampling of unphysical structures. We show that CGnets can capture all-atom explicit-solvent free energy surfaces with models using only a few coarse-grained beads and no solvent, while classical coarse-graining methods fail to capture crucial features of the free energy surface. Thus, CGnets are able to capture multibody terms that emerge from the dimensionality reduction. ; This work was supported by the National Science Foundation (CHE-1265929, CHE-1738990, and PHY-1427654), the Welch Foundation (C-1570), the MATH+ excellence cluster (AA1-6, EF1-2), the Deutsche Forschungsgemeinschaft (SFB 1114/C03, SFB 958/A04, TRR 186/A12), the European Commission (ERC CoG 772230 "ScaleCell"), the Einstein Foundation Berlin (Einstein Visiting Fellowship to C.C.), and the Alexander von Humboldt foundation (Postdoctoral fellowship to S.O.). Simulations have been performed on the computer clusters of the Center for Research Computing at Rice University, supported in part by the Big-Data Private-Cloud Research Cyberinfrastructure MRI-award (NSF Grant CNS-1338099), and on the clusters of the Department of Mathematics and Computer Science at Freie Universität, Berlin. G.D.F. acknowledges support from MINECO (Unidad de Excelencia María de Maeztu MDM-2014-0370 and BIO2017-82628-P) and FEDER. This project has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement 675451 (CompBioMed Project). We thank the GPUGRID donors for their compute time.